Research on monocular-vision indoor location technology for olfactory-based searching robots

被引：0
|
作者：
Miao R. [1 ]
Li J. [2 ]
Jiu H. [3 ]
Pang S. [1 ]
机构：
[1] School of Shipbuilding Engineering, Harbin Engineering University, Harbin
[2] School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai
[3] Institute of Light Industry, Harbin University of Commerce, Harbin
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2019年 / 47卷 / 04期
关键词：
Image processing; K-fold cross validation; Olfactory-based searching robot; Support vector machine; Target recognition; Vision localization;
D O I：
10.13245/j.hust.190404
中图分类号：
学科分类号：
摘要：
In order to localize olfactory-based searching mobile robots in the indoor environments, a monocular-vision localization algorithm was proposed based on least square support vector machine (LS-SVM) method. By using Gaussian kernel LS-SVM classifier, the non-linear mapping between robot real positions to pixels in the image could be translated into linear mapping with Gaussian kernel. Then, k-fold cross validation was used to seek the optimized parameters after linear mapping model training. Finally, continuously moving robot was identified based on greyscale level in real time from sequential images reordered by a single camera. The real-time position of robot in the indoor environment was calculated from the vehicle＇s geometric center pixel on every image using the linear mapping model developed herein. Experiment results show that the proposed localization algorithm can provide high precision real-time position information for robots in the indoor environment without error accumulated with time. The accuracy and reliability of the algorithm were fully verified. © 2019, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：19 / 24
页数：5
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